Overview

Dataset statistics

Number of variables15
Number of observations2161
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory253.4 KiB
Average record size in memory120.1 B

Variable types

Numeric11
Categorical4

Alerts

時間(min) is highly correlated with 轉速(rpm) and 13 other fieldsHigh correlation
轉速(rpm) is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T1 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T3 is highly correlated with 時間(min) and 12 other fieldsHigh correlation
T4 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T7 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T9 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T10 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T11 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T12 is highly correlated with 時間(min) and 13 other fieldsHigh correlation
Z is highly correlated with 時間(min) and 13 other fieldsHigh correlation
T2 is highly correlated with 時間(min) and 12 other fieldsHigh correlation
T5 is highly correlated with 時間(min) and 12 other fieldsHigh correlation
T8 is highly correlated with 時間(min) and 12 other fieldsHigh correlation
T6 is highly correlated with 時間(min) and 9 other fieldsHigh correlation
時間(min) is uniformly distributed Uniform
時間(min) has unique values Unique
轉速(rpm) has 583 (27.0%) zeros Zeros

Reproduction

Analysis started2022-11-11 03:25:16.375859
Analysis finished2022-11-11 03:25:24.381704
Duration8.01 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

時間(min)
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct2161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.99666667
Minimum-0.003333333333
Maximum179.9966667
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size17.0 KiB
2022-11-11T11:25:24.409585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.003333333333
5-th percentile8.996666667
Q144.99666667
median89.99666667
Q3134.9966667
95-th percentile170.9966667
Maximum179.9966667
Range180
Interquartile range (IQR)90

Descriptive statistics

Standard deviation51.99760723
Coefficient of variation (CV)0.5777725904
Kurtosis-1.2
Mean89.99666667
Median Absolute Deviation (MAD)45
Skewness-1.964619272 × 10-16
Sum194482.7967
Variance2703.751157
MonotonicityStrictly increasing
2022-11-11T11:25:24.465420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0033333333331
 
< 0.1%
119.66333331
 
< 0.1%
120.831
 
< 0.1%
120.74666671
 
< 0.1%
120.66333331
 
< 0.1%
120.581
 
< 0.1%
120.49666671
 
< 0.1%
120.41333331
 
< 0.1%
120.331
 
< 0.1%
120.24666671
 
< 0.1%
Other values (2151)2151
99.5%
ValueCountFrequency (%)
-0.0033333333331
< 0.1%
0.081
< 0.1%
0.16333333331
< 0.1%
0.24666666671
< 0.1%
0.331
< 0.1%
0.41333333331
< 0.1%
0.49666666671
< 0.1%
0.581
< 0.1%
0.66333333331
< 0.1%
0.74666666671
< 0.1%
ValueCountFrequency (%)
179.99666671
< 0.1%
179.91333331
< 0.1%
179.831
< 0.1%
179.74666671
< 0.1%
179.66333331
< 0.1%
179.581
< 0.1%
179.49666671
< 0.1%
179.41333331
< 0.1%
179.331
< 0.1%
179.24666671
< 0.1%

轉速(rpm)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10236.92735
Minimum0
Maximum20000
Zeros583
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-11-11T11:25:24.518243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12000
Q316000
95-th percentile20000
Maximum20000
Range20000
Interquartile range (IQR)16000

Descriptive statistics

Standard deviation6995.921206
Coefficient of variation (CV)0.6834004939
Kurtosis-1.228557519
Mean10236.92735
Median Absolute Deviation (MAD)4000
Skewness-0.3929189446
Sum22122000
Variance48942913.52
MonotonicityNot monotonic
2022-11-11T11:25:24.562767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0583
27.0%
11000122
 
5.6%
12000122
 
5.6%
13000122
 
5.6%
15000122
 
5.6%
16000122
 
5.6%
17000122
 
5.6%
18000122
 
5.6%
19000122
 
5.6%
20000122
 
5.6%
Other values (4)480
22.2%
ValueCountFrequency (%)
0583
27.0%
8000120
 
5.6%
9000120
 
5.6%
10000120
 
5.6%
11000122
 
5.6%
12000122
 
5.6%
13000122
 
5.6%
14000120
 
5.6%
15000122
 
5.6%
16000122
 
5.6%
ValueCountFrequency (%)
20000122
5.6%
19000122
5.6%
18000122
5.6%
17000122
5.6%
16000122
5.6%
15000122
5.6%
14000120
5.6%
13000122
5.6%
12000122
5.6%
11000122
5.6%

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.45941694
Minimum24.3
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-11-11T11:25:24.612599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.3
5-th percentile24.5
Q124.9
median25.4
Q326
95-th percentile26.7
Maximum27
Range2.7
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.6794370169
Coefficient of variation (CV)0.02668706116
Kurtosis-0.8238529971
Mean25.45941694
Median Absolute Deviation (MAD)0.5
Skewness0.4101336566
Sum55017.8
Variance0.4616346599
MonotonicityNot monotonic
2022-11-11T11:25:24.664585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
24.9167
 
7.7%
24.8146
 
6.8%
25.5132
 
6.1%
25.3131
 
6.1%
24.7127
 
5.9%
25.7125
 
5.8%
25.1112
 
5.2%
24.6105
 
4.9%
26.2105
 
4.9%
25.490
 
4.2%
Other values (18)921
42.6%
ValueCountFrequency (%)
24.319
 
0.9%
24.439
 
1.8%
24.558
 
2.7%
24.6105
4.9%
24.7127
5.9%
24.8146
6.8%
24.9167
7.7%
2578
3.6%
25.1112
5.2%
25.276
3.5%
ValueCountFrequency (%)
273
 
0.1%
26.967
3.1%
26.810
 
0.5%
26.748
2.2%
26.659
2.7%
26.553
2.5%
26.450
2.3%
26.332
 
1.5%
26.2105
4.9%
26.155
2.5%

T2
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
24.4
996 
24.3
678 
24.6
236 
24.5
147 
24.7
104 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8644
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.3
2nd row24.3
3rd row24.3
4th row24.3
5th row24.3

Common Values

ValueCountFrequency (%)
24.4996
46.1%
24.3678
31.4%
24.6236
 
10.9%
24.5147
 
6.8%
24.7104
 
4.8%

Length

2022-11-11T11:25:24.712734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:25:24.768398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
24.4996
46.1%
24.3678
31.4%
24.6236
 
10.9%
24.5147
 
6.8%
24.7104
 
4.8%

Most occurring characters

ValueCountFrequency (%)
43157
36.5%
22161
25.0%
.2161
25.0%
3678
 
7.8%
6236
 
2.7%
5147
 
1.7%
7104
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6483
75.0%
Other Punctuation2161
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
43157
48.7%
22161
33.3%
3678
 
10.5%
6236
 
3.6%
5147
 
2.3%
7104
 
1.6%
Other Punctuation
ValueCountFrequency (%)
.2161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8644
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
43157
36.5%
22161
25.0%
.2161
25.0%
3678
 
7.8%
6236
 
2.7%
5147
 
1.7%
7104
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII8644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43157
36.5%
22161
25.0%
.2161
25.0%
3678
 
7.8%
6236
 
2.7%
5147
 
1.7%
7104
 
1.2%

T3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.34183248
Minimum24.1
Maximum24.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-11-11T11:25:24.810682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.1
5-th percentile24.2
Q124.3
median24.3
Q324.4
95-th percentile24.6
Maximum24.7
Range0.6
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1199504978
Coefficient of variation (CV)0.004927751347
Kurtosis0.4639104883
Mean24.34183248
Median Absolute Deviation (MAD)0
Skewness1.103946131
Sum52602.7
Variance0.01438812193
MonotonicityNot monotonic
2022-11-11T11:25:24.847792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
24.31272
58.9%
24.2291
 
13.5%
24.6259
 
12.0%
24.4196
 
9.1%
24.5115
 
5.3%
24.120
 
0.9%
24.78
 
0.4%
ValueCountFrequency (%)
24.120
 
0.9%
24.2291
 
13.5%
24.31272
58.9%
24.4196
 
9.1%
24.5115
 
5.3%
24.6259
 
12.0%
24.78
 
0.4%
ValueCountFrequency (%)
24.78
 
0.4%
24.6259
 
12.0%
24.5115
 
5.3%
24.4196
 
9.1%
24.31272
58.9%
24.2291
 
13.5%
24.120
 
0.9%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.92100879
Minimum24.6
Maximum25.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-11-11T11:25:24.890474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.6
5-th percentile24.6
Q124.7
median24.9
Q325.1
95-th percentile25.3
Maximum25.4
Range0.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2254277835
Coefficient of variation (CV)0.009045692547
Kurtosis-1.171693658
Mean24.92100879
Median Absolute Deviation (MAD)0.2
Skewness0.1338026629
Sum53854.3
Variance0.05081768557
MonotonicityNot monotonic
2022-11-11T11:25:24.932004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
25.1322
14.9%
24.6317
14.7%
24.7316
14.6%
24.8289
13.4%
25.2276
12.8%
24.9270
12.5%
25219
10.1%
25.3125
 
5.8%
25.427
 
1.2%
ValueCountFrequency (%)
24.6317
14.7%
24.7316
14.6%
24.8289
13.4%
24.9270
12.5%
25219
10.1%
25.1322
14.9%
25.2276
12.8%
25.3125
 
5.8%
25.427
 
1.2%
ValueCountFrequency (%)
25.427
 
1.2%
25.3125
 
5.8%
25.2276
12.8%
25.1322
14.9%
25219
10.1%
24.9270
12.5%
24.8289
13.4%
24.7316
14.6%
24.6317
14.7%

T5
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
25.0
905 
25.1
729 
25.2
527 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8644
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25.1
2nd row25.1
3rd row25.1
4th row25.1
5th row25.1

Common Values

ValueCountFrequency (%)
25.0905
41.9%
25.1729
33.7%
25.2527
24.4%

Length

2022-11-11T11:25:24.978497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:25:25.023908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
25.0905
41.9%
25.1729
33.7%
25.2527
24.4%

Most occurring characters

ValueCountFrequency (%)
22688
31.1%
52161
25.0%
.2161
25.0%
0905
 
10.5%
1729
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6483
75.0%
Other Punctuation2161
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
22688
41.5%
52161
33.3%
0905
 
14.0%
1729
 
11.2%
Other Punctuation
ValueCountFrequency (%)
.2161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8644
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
22688
31.1%
52161
25.0%
.2161
25.0%
0905
 
10.5%
1729
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII8644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22688
31.1%
52161
25.0%
.2161
25.0%
0905
 
10.5%
1729
 
8.4%

T6
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
25.5
1093 
25.4
1063 
25.6
 
5

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8644
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25.5
2nd row25.5
3rd row25.5
4th row25.5
5th row25.5

Common Values

ValueCountFrequency (%)
25.51093
50.6%
25.41063
49.2%
25.65
 
0.2%

Length

2022-11-11T11:25:25.065705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:25:25.110600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
25.51093
50.6%
25.41063
49.2%
25.65
 
0.2%

Most occurring characters

ValueCountFrequency (%)
53254
37.6%
22161
25.0%
.2161
25.0%
41063
 
12.3%
65
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6483
75.0%
Other Punctuation2161
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
53254
50.2%
22161
33.3%
41063
 
16.4%
65
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.2161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8644
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
53254
37.6%
22161
25.0%
.2161
25.0%
41063
 
12.3%
65
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53254
37.6%
22161
25.0%
.2161
25.0%
41063
 
12.3%
65
 
0.1%

T7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.25677927
Minimum24.1
Maximum24.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-11-11T11:25:25.146780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.1
5-th percentile24.1
Q124.2
median24.2
Q324.3
95-th percentile24.6
Maximum24.6
Range0.5
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1233468155
Coefficient of variation (CV)0.005085045058
Kurtosis1.732942192
Mean24.25677927
Median Absolute Deviation (MAD)0
Skewness1.50317555
Sum52418.9
Variance0.0152144369
MonotonicityNot monotonic
2022-11-11T11:25:25.187670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
24.21229
56.9%
24.3402
 
18.6%
24.1197
 
9.1%
24.6136
 
6.3%
24.4113
 
5.2%
24.584
 
3.9%
ValueCountFrequency (%)
24.1197
 
9.1%
24.21229
56.9%
24.3402
 
18.6%
24.4113
 
5.2%
24.584
 
3.9%
24.6136
 
6.3%
ValueCountFrequency (%)
24.6136
 
6.3%
24.584
 
3.9%
24.4113
 
5.2%
24.3402
 
18.6%
24.21229
56.9%
24.1197
 
9.1%

T8
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.0 KiB
24.5
681 
24.4
653 
24.3
458 
24.6
357 
24.7
 
12

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8644
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.3
2nd row24.3
3rd row24.3
4th row24.3
5th row24.3

Common Values

ValueCountFrequency (%)
24.5681
31.5%
24.4653
30.2%
24.3458
21.2%
24.6357
16.5%
24.712
 
0.6%

Length

2022-11-11T11:25:25.230466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:25:25.348422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
24.5681
31.5%
24.4653
30.2%
24.3458
21.2%
24.6357
16.5%
24.712
 
0.6%

Most occurring characters

ValueCountFrequency (%)
42814
32.6%
22161
25.0%
.2161
25.0%
5681
 
7.9%
3458
 
5.3%
6357
 
4.1%
712
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6483
75.0%
Other Punctuation2161
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
42814
43.4%
22161
33.3%
5681
 
10.5%
3458
 
7.1%
6357
 
5.5%
712
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.2161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8644
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
42814
32.6%
22161
25.0%
.2161
25.0%
5681
 
7.9%
3458
 
5.3%
6357
 
4.1%
712
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42814
32.6%
22161
25.0%
.2161
25.0%
5681
 
7.9%
3458
 
5.3%
6357
 
4.1%
712
 
0.1%

T9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.62434058
Minimum23.2
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-11-11T11:25:25.429740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.2
5-th percentile23.2
Q123.5
median23.6
Q323.8
95-th percentile23.9
Maximum24
Range0.8
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2266908538
Coefficient of variation (CV)0.009595647888
Kurtosis-0.8089366472
Mean23.62434058
Median Absolute Deviation (MAD)0.2
Skewness-0.4877042061
Sum51052.2
Variance0.05138874321
MonotonicityNot monotonic
2022-11-11T11:25:25.471220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
23.6523
24.2%
23.9478
22.1%
23.7377
17.4%
23.2215
9.9%
23.8187
 
8.7%
23.3183
 
8.5%
23.4112
 
5.2%
23.574
 
3.4%
2412
 
0.6%
ValueCountFrequency (%)
23.2215
9.9%
23.3183
 
8.5%
23.4112
 
5.2%
23.574
 
3.4%
23.6523
24.2%
23.7377
17.4%
23.8187
 
8.7%
23.9478
22.1%
2412
 
0.6%
ValueCountFrequency (%)
2412
 
0.6%
23.9478
22.1%
23.8187
 
8.7%
23.7377
17.4%
23.6523
24.2%
23.574
 
3.4%
23.4112
 
5.2%
23.3183
 
8.5%
23.2215
9.9%

T10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.48611754
Minimum24
Maximum24.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-11-11T11:25:25.515534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile24.1
Q124.4
median24.5
Q324.7
95-th percentile24.8
Maximum24.8
Range0.8
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2316242229
Coefficient of variation (CV)0.009459409909
Kurtosis-0.7238165699
Mean24.48611754
Median Absolute Deviation (MAD)0.2
Skewness-0.5212434701
Sum52914.5
Variance0.05364978062
MonotonicityNot monotonic
2022-11-11T11:25:25.555399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24.5637
29.5%
24.8308
14.3%
24.1299
13.8%
24.6299
13.8%
24.7264
12.2%
24.4113
 
5.2%
24.395
 
4.4%
24.289
 
4.1%
2457
 
2.6%
ValueCountFrequency (%)
2457
 
2.6%
24.1299
13.8%
24.289
 
4.1%
24.395
 
4.4%
24.4113
 
5.2%
24.5637
29.5%
24.6299
13.8%
24.7264
12.2%
24.8308
14.3%
ValueCountFrequency (%)
24.8308
14.3%
24.7264
12.2%
24.6299
13.8%
24.5637
29.5%
24.4113
 
5.2%
24.395
 
4.4%
24.289
 
4.1%
24.1299
13.8%
2457
 
2.6%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.24127719
Minimum23.4
Maximum24.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-11-11T11:25:25.600587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.4
5-th percentile23.5
Q124.1
median24.3
Q324.5
95-th percentile24.8
Maximum24.8
Range1.4
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3993618028
Coefficient of variation (CV)0.01647445387
Kurtosis-0.7179529041
Mean24.24127719
Median Absolute Deviation (MAD)0.2
Skewness-0.4454478236
Sum52385.4
Variance0.1594898495
MonotonicityNot monotonic
2022-11-11T11:25:25.642540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
24.3342
15.8%
24.4271
12.5%
24.8267
12.4%
24.1227
10.5%
24.2214
9.9%
23.6185
8.6%
24.7172
8.0%
23.5147
6.8%
24.694
 
4.3%
24.573
 
3.4%
Other values (5)169
7.8%
ValueCountFrequency (%)
23.415
 
0.7%
23.5147
6.8%
23.6185
8.6%
23.753
 
2.5%
23.830
 
1.4%
23.936
 
1.7%
2435
 
1.6%
24.1227
10.5%
24.2214
9.9%
24.3342
15.8%
ValueCountFrequency (%)
24.8267
12.4%
24.7172
8.0%
24.694
 
4.3%
24.573
 
3.4%
24.4271
12.5%
24.3342
15.8%
24.2214
9.9%
24.1227
10.5%
2435
 
1.6%
23.936
 
1.7%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.39551134
Minimum22.9
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-11-11T11:25:25.687966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.9
5-th percentile23
Q123.3
median23.4
Q323.6
95-th percentile23.7
Maximum23.8
Range0.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2430196418
Coefficient of variation (CV)0.01038744733
Kurtosis-0.7011347725
Mean23.39551134
Median Absolute Deviation (MAD)0.2
Skewness-0.521463071
Sum50557.7
Variance0.05905854628
MonotonicityNot monotonic
2022-11-11T11:25:25.727215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
23.4691
32.0%
23.7403
18.6%
23292
13.5%
23.5287
13.3%
23.6169
 
7.8%
22.984
 
3.9%
23.175
 
3.5%
23.273
 
3.4%
23.364
 
3.0%
23.823
 
1.1%
ValueCountFrequency (%)
22.984
 
3.9%
23292
13.5%
23.175
 
3.5%
23.273
 
3.4%
23.364
 
3.0%
23.4691
32.0%
23.5287
13.3%
23.6169
 
7.8%
23.7403
18.6%
23.823
 
1.1%
ValueCountFrequency (%)
23.823
 
1.1%
23.7403
18.6%
23.6169
 
7.8%
23.5287
13.3%
23.4691
32.0%
23.364
 
3.0%
23.273
 
3.4%
23.175
 
3.5%
23292
13.5%
22.984
 
3.9%

Z
Real number (ℝ)

HIGH CORRELATION

Distinct187
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.31008792
Minimum-0.3
Maximum63.5
Zeros1
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size17.0 KiB
2022-11-11T11:25:25.777510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.3
5-th percentile11.6
Q129.3
median37
Q346.1
95-th percentile59.3
Maximum63.5
Range63.8
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation13.42452358
Coefficient of variation (CV)0.3598094866
Kurtosis-0.4419742555
Mean37.31008792
Median Absolute Deviation (MAD)8.8
Skewness-0.1931668584
Sum80627.1
Variance180.2178334
MonotonicityNot monotonic
2022-11-11T11:25:25.835044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.876
 
3.5%
32.763
 
2.9%
3355
 
2.5%
36.153
 
2.5%
42.842
 
1.9%
37.936
 
1.7%
37.635
 
1.6%
43.135
 
1.6%
46.133
 
1.5%
38.832
 
1.5%
Other values (177)1701
78.7%
ValueCountFrequency (%)
-0.31
 
< 0.1%
01
 
< 0.1%
5.51
 
< 0.1%
5.83
 
0.1%
6.54
0.2%
6.86
0.3%
7.17
0.3%
7.42
 
0.1%
7.71
 
< 0.1%
88
0.4%
ValueCountFrequency (%)
63.51
 
< 0.1%
63.21
 
< 0.1%
62.94
 
0.2%
62.61
 
< 0.1%
62.310
0.5%
6219
0.9%
61.713
0.6%
61.43
 
0.1%
61.111
0.5%
60.84
 
0.2%

Interactions

2022-11-11T11:25:23.591259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:17.668452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.257842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.849947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.457532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.014470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.657733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.200279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.844239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.424213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.055651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.640090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:17.721316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.310775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.961713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.507453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.066296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.706917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.253420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.897001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.475490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.104649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.694257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:17.789463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.366763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.014695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.560429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.123105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.758944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.310229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.951517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.528273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.153781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.742179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:17.837500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.419829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.062534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.607915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.174542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.808215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.360548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.002905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.577269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.199675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.791926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:17.888766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.472893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.110172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.658744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.290152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.857051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.412374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.055726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.629326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.249856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.842755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:17.943899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.527860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.161729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.714133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.347043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.908926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.466192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.111538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.686134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.302678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.890615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:17.994949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.580680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.209238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.761971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.397386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.955203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.583796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.162628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.736054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.350577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.944433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.048766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.637524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.262086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.815208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.451585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.007029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.637825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.217412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.791167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.400581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.997255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.103473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.691720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.313885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.867497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.505453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.057761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.692656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.272227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.907774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.451939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:24.049081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.157354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.745837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.364679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.918875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.559088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.108420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.744481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.324677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.958603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.500775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:24.095093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.207311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:18.797820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.409608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:19.965153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:20.607817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.153171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:21.794313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:22.373405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.006812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:23.544416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:25:25.891295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:25:25.966532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:25:26.045846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:25:26.125533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:25:26.200281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-11T11:25:26.256927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:25:24.241428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:25:24.347201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

時間(min)轉速(rpm)T1T2T3T4T5T6T7T8T9T10T11T12Z
0-0.003333024.324.324.324.625.125.524.324.323.224.023.622.90.0
10.080000024.324.324.324.625.125.524.324.323.224.023.622.9-0.3
20.163333800024.324.324.324.625.125.524.324.323.224.023.622.95.5
30.246667800024.324.324.324.625.125.524.324.323.224.023.622.96.5
40.330000800024.324.324.324.625.125.524.324.323.224.023.622.95.8
50.413333800024.324.324.324.625.125.624.324.323.224.023.622.95.8
60.496667800024.324.324.324.625.125.624.324.323.224.023.622.95.8
70.580000800024.324.324.324.625.125.624.324.323.224.023.622.97.1
80.663333800024.324.324.324.625.125.624.324.323.224.023.622.97.4
90.746667800024.324.324.324.625.125.624.324.323.224.023.622.97.1

Last rows

時間(min)轉速(rpm)T1T2T3T4T5T6T7T8T9T10T11T12Z
2151179.246667024.624.624.625.025.225.524.624.623.724.624.323.532.4
2152179.330000024.624.624.625.025.225.524.624.623.724.624.323.531.8
2153179.413333024.624.624.625.025.225.524.624.623.724.624.323.531.5
2154179.496667024.624.624.625.025.225.524.624.623.724.624.323.532.1
2155179.580000024.624.624.625.025.225.524.624.623.724.624.323.532.1
2156179.663333024.624.624.625.025.225.524.624.623.724.624.323.531.5
2157179.746667024.624.624.625.025.225.524.624.623.724.624.323.532.4
2158179.830000024.624.624.625.025.225.524.624.623.724.624.323.531.8
2159179.913333024.624.624.625.025.225.524.624.623.724.624.323.531.5
2160179.996667024.624.724.625.025.225.524.624.623.724.624.323.531.8